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基于可解释机器学习的多参数MRI息地放射学,用于在直肠癌中进行微卫星不稳定性的手术前评估

Yueyan Wang¹, Bo Xie¹, Kai Wang¹

¹Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu 233000, China (Y.W., B.X., K.W., W.Z., Y.M.); Graduate School of Bengbu Medical University, Bengbu 233000, China (Y.W., B.X., K.W., W.Z.).

Academic radiology

|February 27, 2025

概括

这项研究开发了一种使用MRI放射学和临床数据的机器学习模型,用于预测直肠癌 (RC) 患者的微卫星不稳定性 (MSI) 状态,改善手术前诊断和治疗计划.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
机器学习机器学习

背景情况:

微卫星不稳定性 (MSI) 是直肠癌 (RC) 的关键生物标志物.
精确的MSI状态的术前评估对于个性化治疗策略至关重要.
目前用于MSI状态确定的方法可能是侵入性的或耗时的.

研究的目的:

构建一个可解释的机器学习模型,用于手术前评估RC患者MSI状态.
整合多参数MRI亚区域息地放射学和临床病理学特征.
与传统方法相比,提高MSI状态预测的准确性.

主要方法:

对291名确诊MSI状态的RC患者的回顾性分析.
从多参数MRI中提取经典和次区域放射学特征,使用K-means集群.
使用后勤回归和特征选开发预测模型.
使用Shapley-Additive-Explanations (SHAP) 算法对特征贡献的量化.

主要成果:

亚区域放射学模型的AUC达到0.848 (培训) 和0.8 (测试).
结合了临床,古典放射学和亚区域放射学特征的组合模型,证明了卓越的性能.
组合模型的AUC为0.908 (训练) 和0.863 (测试).

结论:

开发并验证了一种强大的组合模型,用于RC中准确的手术前MSI状态确定.
可解释的模型,用SHAP可视化,促进个性化治疗规划.
这种方法有可能提高RC患者的生存率.

关键词:

微卫星的不稳定性是微观的无线电学 (Radiomics) 是一种无线电学.这是直肠癌.沙普利添加剂的解释亚区域亚区域是指一个亚区域.

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